#vectorindex

[edoxidubai]

Optimize your AI search performance with vector index hygiene keep your data clean, accurate & SEO-ready for smarter results every time.

learn more: www.edoxi.com/dubai

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Advanced Retrieval Techniques

Retrieval-Augmented Generation with Citations - Explore how augmentation with citations can significantly improve the depth and reliability of generated content.

Similarity Metrics for Vector Search - Understand different metrics that drive the effectiveness of vector searches, crucial for refining retrieval systems.

Local Agentic RAG with Langraph and Llama3 - Discover the integration of local datasets with advanced retrieval frameworks for enhanced performance.

Multimodal RAG with CLIP, Llama3, and Milvus - A deep dive into a multimodal approach, combining textual and visual data for rich content generation.

Practical Guides for Developers

A Beginner's Guide to Using Llama 3 with Ollama, Milvus, LangChain - Perfect for developers new to our frameworks, offering step-by-step guidance.

Getting Started with a Milvus Connection and Getting Started: Pgvector Guide for Developers Exploring Vector Databases - These guides are essential for setting up and beginning work with vector databases.

Educational Articles on Embedding Techniques and Applications

Sparse and Dense Embeddings - A look at different embedding types, offering insights into their use-cases and benefits.

Mastering BM25: A Deep Dive into the Algorithm and Application in Milvus - An in-depth exploration of BM25, a core algorithm for understanding document relevance.

Comparing SPLADE Sparse Vectors with BM25 - Comparative analysis that helps in selecting the right tool for specific retrieval tasks.

Training Your Own Text Embedding Model - Empower your projects by creating custom models tailored to your specific data needs.

Implementing and Optimizing RAG

Guide to Chunking Strategies for RAG and Experimenting with Different Chunking Strategies via LangChain - Both resources provide strategic insights into segmenting text for better retrieval outcomes.

Optimize RAG with Rerankers: The Role and Tradeoffs - Detailed discussion on the optimization of retrieval systems for balance between accuracy and performance.

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Training Your Own Text Embedding Model - Zilliz blog

Explore how to train your text embedding model using the `sentence-transformers` library and generate our training data by leveraging a pre-

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Advanced Retrieval Augmented Generation (RAG) Apps with LlamaIndex - Zilliz blog

Laurie’s presentation showcases basic and advanced application frameworks for RAG, which we can build with minimal lines of code using Llama